Systems Biology and Longevity - University of Liverpool

13 Pages·2010·0.74 MB·English

802 Current Pharmaceutical Design, 2010, 16, 802-813
Systems Biology and Longevity: An Emerging Approach to Identify Innovative Anti-
Aging Targets and Strategies
E. Cevenini1,2, E. Bellavista1,2, P. Tieri1,2, G. Castellani1,3, F. Lescai1,2, M. Francesconi1,3, M. Mishto1,2,4, A.
Santoro1,2, S. Valensin1,2, S. Salvioli1,2, M. Capri1,2, A. Zaikin5, D. Monti6, J. P. de Magalhães7 and C.
Franceschi1,2,*
1C.I.G. - Interdepartmental Centre “L. Galvani”, University of Bologna, Bologna, Italy; 2Department of Experimental Pathology,
University of Bologna, Bologna, Italy; 3Department of Physics, University of Bologna, Bologna, Italy; 4Institute of Biochemistry,
Medical Faculty Charité, Berlin, Germany; 5Departments of Mathematics and Institute of Women Health, University College London,
London, United Kingdom; 6Department of Experimental Pathology and Oncology, University of Florence, Florence, Italy; 7School of
Biological Sciences, University of Liverpool, Liverpool, United Kingdom
Abstract: Human aging and longevity are complex and multi-factorial traits that result from a combination of environmental, genetic,
epigenetic and stochastic factors, each contributing to the overall phenotype. The multi-factorial process of aging acts at different levels
of complexity, from molecule to cell, from organ to organ systems and finally to organism, giving rise to the dynamic “aging mosaic”. At
present, an increasing amount of experimental data on genetics, genomics, proteomics and other –omics are available thanks to new high-
throughput technologies but a comprehensive model for the study of human aging and longevity is still lacking. Systems biology
represents a strategy to integrate and quantify the existing knowledge from different sources into predictive models, to be later tested and
then implemented with new experimental data for validation and refinement in a recursive process. The ultimate goal is to compact the
new acquired knowledge into a single picture, ideally able to characterize the phenotype at systemic/organism level. In this review we
will briefly discuss the aging phenotype in a systems biology perspective, showing four specific examples at different levels of
complexity, from a systemic process (inflammation) to a cascade-process pathways (coagulation) and from cellular organelle
(proteasome) to single gene-network (PON-1), which could also represent targets for anti-aging strategies.
Keywords: Aging mosaic, longevity, systems biology, inflammaging, coagulation, proteasome, PON-1.
1. AGING AND LONGEVITY To this regard, it has been reported that the identification of
genetic variants underlying complex traits and diseases (such as
1.1. Aging as a Complex Trait psychopathology, hypertension, cardiovascular diseases [5] and
The study on human aging and longevity has become a very hot neurodegenerative diseases [6]) may take advantage from the study
topic in the last years because of the so-called revolution in of gene expression in particular environmental conditions [5]. The
demography, which led to the remarkable increase in the number of studies on human aging and longevity are further complicated by
people over the age of 65 or 80 years living in Western countries the fact that human populations are heterogeneous from the point of
but also in some emerging countries such as China and India [1]. view of genetic pool, life style, cultural habits, education, economic
The data collected during the last 20 years in different models status and social network. All these components are different from
suggest that the picture of the aging phenotype is fragmented and population to population, and each population is characterised by a
above all qualitative and that we are far from an exhaustive and unique combination of them. This fact renders the studies difficult
quantitative scenario. This incomplete knowledge comes out from to compare and the results very often discordant. Finally, all these
several bias: 1. few studies evaluate many parameters at the same considerations also apply to gender difference, since gender appears
time in the same individual; 2. the collected data are not of high- to be a crucial player in the cross-talk between genes, environment
dimensionality; 3. longitudinal studies, which are the most infor- and health [7]. The development of effective and realistic strategies
mative, are scanty. Another factor contributing to the complexity of for aging intervention, prevention and therapies may be facilitated
the problem is that human aging and longevity are complex and by this integrated and multi-faced view. Indeed, it has been
multi-factorial traits, generally considered as the result of the proposed that the manipulation of both genes and environment at
combination of environment, genetics (and epigenetics) and the same time can open up novel possibilities of aging intervention
stochasticity, each making variable contributions to the overall and prevention [8].
phenotype [2-4]. It is hypothesised that the importance of each
component changes with the passing of time, as shown in Fig. (1). 1.2. Aging as a Mosaic
In this cartoon the relative importance of each component as well as It is conceivable that longevity could be achieved by different
the level of interaction with the other components are represented strategies and by different combinations of genetics, epigenetics,
as colour tones and overlapping of circles. According to this environment and stochasticity and the result, i.e. the aging
hypothesis, genetics is less important in the early adult phase of life phenotype, is very heterogeneous. Moreover, it is emerging that the
and becomes more important in old age. Moreover, these different multi-factorial process of aging does not occur only at organism
components interact with each other, in particular genetics and level, but it also acts differently in each organ system, organ, tissue
environment. and even in each single cell of the body, determining a different
aging rate for each of them. In mice it has been observed that
different tissues age in a coordinated fashion [9], while in humans

this is still to be ascertained. As a consequence of these different
*Address correspondence to this author at the Department of Experimental aging rates, the aged body could be considered as a mosaic of
Pathology, University of Bologna, Via San Giacomo 12, 40126 Bologna, tissues and organs displaying a different level of senescence, a
Italy; Tel: [phone]; Fax: [phone]; situation we proposed to indicate as “aging mosaic” [10], as
E-mail: [email protected]

1381-6128/10 $55.00+.00 © 2010 Bentham Science Publishers Ltd.
Systems Biology and Longevity Current Pharmaceutical Design, 2010, Vol. 16, No. 7 803

Fig. (1). The changes on relative importance and interaction between the main determinants of healthy aging and longevity. The colour tones are a
function of the relative importance of each component (E=environment, G=genetics and S=stochasticity). The increase intensity of the colour (e.g. from
yellow to red) means increased importance. The overlapping of the circles is a function of the interactions between these components. The in utero
environment has been highlighted in cyan as it is profoundly different from the post natal one, highlighted in green tones.
exemplified in Fig. (2). In this figure, we represented the human that low-grade stressors can be assimilated to what is referred to as
body as a mosaic of 12 organ systems (indicated as rectangles) functional or constructive noise. The role of noise in biological
according to Hunter and Borg [11], each of them displaying a systems is under investigation, and, for example, it is now evident
different level of senescence (represented by black dots). that the stochastic or inherently random nature of the biochemical
reactions of gene expression may contribute to the phenotypic
Thus, even in the same individual, the aging process appears to
follow different trajectories in different organs, tissues and cells, variability in individuals [15]. Thus, noise, which was often
considered “unimportant” by traditional statistical methods and
which are variably affected and accumulate unrepaired damages at
models, should be taken into account during data analysis, since it
different rates. An example, among many others, is the brain where
different regions, such as cortex, hippocampus and cerebellum, may hide un-analyzed information [16]. For example, in gene
expression analysis, only genes whose expression profile differs
show different levels of neurodegeneration and inflammation in the
from an established threshold were evaluated and the others were
same subject [12,13]. In mice, a great heterogeneity in the amount
of transcriptional changes with age in different tissues was found classified as “noise” and erroneously excluded [15]. It is also
important to note that noise-induced phenomena have been
[9]. Based on the pattern of age-related transcriptional changes,
experimentally detected in many levels of biological functionality,
three aging patterns have been proposed: (1) a pattern common to
neural tissues, (2) a pattern for vascular tissues, and (3) a pattern for i.e. in plankton detection by paddle fish [17], in the retrieval
processes of the human memory [18], and in human brain waves
steroid-responsive tissues [9].
[19]. How can noise potentially play a constructive role in the
Moreover, this mosaic is dynamic, meaning that it changes with
system dynamics of main components and compartments of a
time, owing to the complex non linear interactions among the
biological organism? As regards the immunoproteasome [20], a key
different components, resulting in a final phenotype that is not
cellular organelle that will be discussed later in this review, it has
easily predictable by the study of its single sub-components.
been addressed whether the protein translocation inside the
Changes in this mosaic derive from different forms of stressors to
proteasome chamber can be driven by fluctuation, and a toy-model
which the body is exposed lifelong. The adaptive response to these
has been derived to show the probable mechanism of translocation
perturbations can follow two trajectories: 1. in case of exposure to a
[21]. Even if at the moment there is no experimental verification of
repeated low grade stimulus, the mosaic is able to positively
the proposed hypothesis, however, it can be expected that noise-
remodel the mechanisms of maintenance which are up-regulated
induced behaviour could play a major role in the immunopro-
and the systemic result is the resistance to stress (hormesis); 2.
teasome functioning, making this aspect worth of deeper
conversely, in case of exposure to a stronger stimulus (over a
investigation.
specific threshold) in terms of intensity and persistence, the
remodelling process still occurs, but the mechanisms of main- Currently, most of the studies focused on human aging refer to
a single district of the mosaic (generally the most easily available
tenance are less efficient and the global result is negative and
detrimental for health and survival [14]. Therefore, a strategy to tissues). For example, experimental results indicate that in vivo cell
increase healthy aging and longevity could be to favour the types from skin of aged individuals are likely to compose a mosaic
made of cells irreversibly growth arrested (replicative senescence)
hormetic response by transforming intense and persistent stressors
into low-grade ones. In this perspective it is also interesting to note and cells maintaining a young proliferative phenotype.
804 Current Pharmaceutical Design, 2010, Vol. 16, No. 7 Cevenini et al.

Fig. (2). Aging as a dynamic mosaic. The body (young or aged) is represented as a square subdivided in 12 rectangles, each of them representing an organ
system. The amount of dots indicates the rate of aging affecting each system.
To this regard, ex vivo studies performed on human diploid middle-aged and extreme long-living individuals. We named this
fibroblasts (HDFs) from young and centenarian subjects show phenomenon Complex Allele Timing [3]. The first example of
interesting results: they are characterized by the same proliferative such kind of trajectories was reported since 1998 on APOB [30]
capacity and the same telomere length [22], but they differ for the and the latest one is represented by Klotho KL-VS polymorphism,
gene expression profile [23]. Moreover, in vitro studies show for for which a significant increase of the heterozygous Klotho
example that the higher level of stress to which these cells have genotype in the class of elderly people compared to young controls
been exposed throughout their life span, the higher proportion of emerged, while no difference was present between centenarians and
the cells of this mosaic will undergo stress-induced premature young controls. Thus, this KL-VS heterozygous genotype is favour-
senescence (SIPS) rather than telomere-shortening dependent able for survival in old people, its beneficial effect decreasing
senescence. All cell types undergoing SIPS in vivo, most notably thereafter, and becoming no more evident at the extreme ages. Such
those in stressful conditions, are likely to participate in the tissutal a non monotonic age-related trajectory of the Klotho KL-VS
changes observed along aging. For instance, HDFs exposed in vivo genotype frequency is compatible with the hypothesis that alleles
and in vitro to pro-inflammatory cytokines display biomarkers of and genotypes involved in aging and longevity may exert their
senescence and might participate in the degradation of the extra- biological effect at specific time windows [31]. It is noteworthy that
cellular matrix observed in aging [24]. In addition, despite support this conceptualization is supported by the use of demographic
for the idea that senescence is a beneficial anticancer mechanism information, that, together with data on genetic markers, allowed us
(i.e. expression of anti-proliferative genes), it is emerging that the to calculate hazard rates, relative risks, and survival functions for
secretome of senescent cells display an inflammatory phenotype candidate genes or genotypes, providing a powerful tool for
that can also be deleterious and might contribute to age-related analyzing their influence on longevity and survival [32]. This
pathologies [25]. modelling approach allowed us to predict trajectories in genetic
variation frequencies that have been subsequently confirmed by
A more comprehensive approach to understand the complexity
experimental data [32,33].
of this mosaic would be the simultaneous study of several organs at
once; at present, this strategy has been applied only to animal Moreover, the role of epigenetics (DNA methylation, histone
models, as reported in mice by Schumacher B and co-workers [26]. post-transcriptional modifications, including methylation, acety-
However it should be taken into account that extrapolation of lation, ubiquitination and phosphorylation) in the aging process
results from experimental animals (mice) to humans is not always should also be considered within this scenario because it includes
straightforward, mainly due to the quantitative differences in regulatory mechanisms that could play a pivotal role in cellular
maintenance and reproduction [27]. homeostasis, age-related diseases, such as human cancer, as well as
lifespan. To this regard, "aging epigenetics" became an emerging
Within the complex scenario of such an interaction of different
tissues and organs, each of them having intrinsic regulatory mecha- discipline [34,35]. The epigenetic alterations that accumulate with
nisms as well as different aging rates, we proposed to go beyond the age, such as the global DNA hypomethylation (above all on
repeated sequences), the promoter hyper methylation of Werner
simplistic assumption of longevity genes playing the same role all
along human life. The interpretation of antagonistic pleiotropy Syndrome genes and lamin genes, aberrant CpG island promoter
[28,29] paved the way for a wider interpretation of genes (and their hypermethylation, DNA methyltransferase (DNMT) overexpression
and histone modifications, could be responsible, at least in part, for
polymorphisms) functioning: the reality is even more complex than
antagonistic pleiotropy is capable to describe. Several experimental the phenotype of aged cells, tissues and organs, being in turn
evidences suggest that the role of genes on physiology and responsible for age-related changes in gene expression [4,36].
However, no data are available on the epigenetic of human
pathology can change at different phases of human life. In genetics,
these evidences are represented by complex trajectories in the (extreme) longevity and the global DNA methylation and the
frequencies of functional polymorphisms in population cohorts of methylation status of specific candidate loci for longevity is at
present lacking, as well as epigenetics applied to population gene-
different ages. The presence of such trajectories suggests that the
same polymorphism can have different biological effects in young, tics studies.
Systems Biology and Longevity Current Pharmaceutical Design, 2010, Vol. 16, No. 7 805
In this complex scenario, where a unique model for the study of In spite of this complex scenario, immunological studies and
human aging and longevity does not exist, a systems biology studies on centenarians revealed that common mechanisms occur-
approach that combines and quantifies genetics, genomics, proteo- red both in very different age-related diseases, such as diabetes and
mics and other –omics fields is necessary to handle the ever neurodegeneration, and in healthy long-living subjects. One is the
increasing amount of experimental data made available by new persistent, low grade inflammatory process which develops with
high-throughput technologies. The final aim we are pursuing is to advancing age and that we called “inflammaging” [46]. Therefore,
use a systems biology perspective to grasp the complexity of anti-inflammatory therapies could efficiently contribute to slow
“aging” and “longevity”; in order to have a healthy old age, a down aging and age-associated pathologies, thus increasing the
systems perspective of medicine could promote the arrangement of possibility to reach longevity. In a general perspective, the pre-
advanced personalized therapies specifically aimed, for example, to vention or the postponement of the aging process could have much
target the individual patient's genetic defects [10,37,38]. greater benefit than those targeted at individual disease [47]. In
addition, combining the already known empirical methods of anti-
2. METHODOLOGICAL APPROACHES TO AGING AS A aging medicine with unique genetic profile of each human (gene-
COMPLEX MOSAIC pass) may render new awarding opportunities for the further
advancements of human longevity programs [48].
2.1. Systems Biology, Aging and Longevity
Aging can be studied at several levels of increasing complexity: 2.2. Resources, Databases and Models
molecular, organelle, cellular, organ, organ system and organism Post-genome technologies, such as microarrays and RNAi, have
level. For a long time the cellular level was used as the most provided researchers with powerful, high-throughput tools to study
integrate level to study aging and longevity, generating a huge aging [49]. To collect, manage, and organize the massive amounts
amount of data, directly transferred to tissues, organs, organ of data generated and help researchers analyze the data, a number
systems and the whole organism. Despite an enormous literature of resources and databases have been assembled. The Human
regarding physiological changes during aging in different organs, Aging Genomic Resources are one of such collections of databases
only few studies have been performed at organ system and and tools, which are of particular interest to systems biology of
organism level and it is emerging that many findings obtained from aging. Databases include GenAge, an accurate database of genes
studies at cellular level often are not informative about the scenario associated with aging in model organisms as well as a selection of
of higher levels of complexity. Only now scientists are trying to candidate aging-related human genes. By providing an annotated
integrate the findings obtained by studies performed at different list of genes associated with aging, GenAge can help researchers
levels into a coherent framework. In this new perspective, systems study how genes interact with each and with the environment to
biology represents a modern science whose aim is to operate at a collectively modulate aging [50].
systemic level, by using the most integrated models (organ and
Microarrays, by measuring gene expression in a high-through-
organism). Thus, it moves biology from a traditional microscopic/
put fashion, allow molecular changes during aging to be charac-
qualitative perspective to a macroscopic/quantitative one, allowing
terized with unprecedented power and several studies using
researchers to integrate and quantify the huge amount but
microarrays have now been performed. One database of gene
fragmented data that nowadays can be obtained by high-throughput
expression profiles during aging, neurodegenerative diseases, and in
technologies [39]. In addition, it also offers tools to develop
interventions affecting aging is the Gene Aging Nexus, which
predictive mathematical models and networks [40], able to evaluate
features a compilation of microarray data and a data analysis
and compare potential explanations for biological data [41]. Thus,
platform (http://gan.usc.edu/) [51]. Another gene expression data-
systems biology represents a strategy to integrate and quantify the
base is AGEMAP, which provides a catalogue of gene expression
existing knowledge and data from different sources into models, to
changes with age in different mouse tissues [9]. These resources
be later tested and then supported with experimental data for
open new opportunities to analyze how multiple genes and
validation and refinement, in a recursive process of “wet and dry”
mechanisms affect aging, how these gene expression changes are
experiments, that will be discussed in the last section of this review.
regulated, and which genes and mechanisms may represent targets
The ultimate goal is to “compact” the new acquired knowledge into
for manipulations.
a single picture, ideally able to characterize the phenotype at
systemic/organism level. One focus of the systems biology approach is to model data
mathematically in order to better interpret the processes under
High-throughput technologies generate complex and high
study, perform computer simulations, and gain novel insights.
dimensionality data that need appropriate statistical analysis, such
Ultimately, the goal of modelling biological processes is to shift
as nonlinear methods [42], and above all a sophisticated inter-
from describing phenotypes to predicting their behaviour and how
pretative approach in order to get insight on their biological
they can be manipulated. In the context of biogerontology, accurate
meaning. As an example, visualization techniques, interaction maps
in silico models of aging would help identify key components (e.g.,
and pathway diagrams can be of great value in order to understand
proteins) that influence aging which would not only guide
how all molecular and cellular components and modules are
experiments but provide novel therapeutic targets to counteract the
intertwined and work together to determine the basic structures and
effects of aging in particular age-related diseases.
the functions of the organism's complex machinery. This approach
has been used by Raza and colleagues [43] to reconstruct a logically Considering the complexity of aging, however, thus far models
represented pathway map, integrating four pathways central to of aging have focused either on specific components of mechanisms
macrophage activation (interferon signalling, NF-(cid:1)B, apoptosis, of aging or at higher levels of abstraction. An example of the
toll-like receptor pathways), and representing them as one integ- former is a mathematical model of the heat shock system, which
rated pathway due to their strongly overlapping interactions. Of has been implicated in aging and age-related diseases [52]. Higher-
course these new methods represent a step further, but we are still level models have focused on gene and protein networks . It is
far from having powerful tools able to capture the complexity of the possible to build proteomic networks for candidate gene prioriti-
problem and to reach the ultimate goal, that is combining and zation. Proteins which interact with proteins associated with aging
quantifying the bulk of available data in specific fields, including are then selected as candidates [53]. Data on genes associated with
multifactorial diseases, such as neurodegenerative disorders aging and their interactions can also be integrated with microarray
[44,45], aging and longevity [38], vaccinology, preventive medi- data to derive candidate genes whose involvement in aging can then
cine, proteolityc systems (proteasome). be tested experimentally [54]. Eventually, the goal is to integrate
different types of data into increasingly more detailed. Interestingly,
806 Current Pharmaceutical Design, 2010, Vol. 16, No. 7 Cevenini et al.
John D. Furber has been assembling a network model of human (T2D), metabolic syndrome (MS), neurodegeneration, sarcopenia
aging at various biological levels. The model is being constructed and cancer, among others.
based on a literature review from which components of aging are An age-related increase in the production of inflammatory
incorporated and their causal relationships described (http://
compounds occurs in the immune system [58,59], brain [60],
www.legendarypharma.com/chartbg.html). adipose tissue [61] and muscle [62], but the possible contribution to
inflammaging of other organs or districts, such as gut microbiota
3. EXAMPLES
and liver, is largely unexplored.
Taking into account that the final aim of the systems biology of
At present a major unsolved problem in biology and medicine is
aging is to characterize the phenotype at systemic/organism level,
the source(s) of the inflammatory stimuli underpinning and
systems biology can be also applied at multiscale levels on the basis
sustaining inflammaging. Moreover, the signaling circuitry and the
of the specific questions to be answered. Thus, studies at molecular,
cross-talk among the various tissues and organs involved in
cellular and organelle levels can be performed and evaluated by
inflammaging are far from being clear. Therefore, systems biology
systems biology because even a single gene can play a role in the
represents the most powerful and comprehensive approach to
definition of a complex phenotype, such as aging and longevity,
characterize the systemic nature of inflammaging, for example by
since it is part of a network where genes and product genes interact.
developing predictive models of inflammaging, urgently needed to
Any perturbation of a single component can modify the whole
set up strategies for the modulation of inflammation, by acting on
network.
strategic targets with global, systemic effect on the whole process.
According to our experience, in this review we propose four
A key point in such a complex framework is the dynamics of
examples where systems biology can be applied at different levels
signaling pathways crucial to inflammation, such as those related to
of complexity, from a systemic process (inflammation) to a
NF-(cid:1)B transcription factor activation. NF-(cid:1)B regulates genes that
cascade-process pathway (coagulation) and from cellular organelle
in turn control cell proliferation, cell survival, as well as innate and
(proteasome) to single gene-network (PON-1), all considered in an
adaptive immune response. In Fig. (4) we provide a partial
integrated vision. We do not treat here other complex processes,
reconstruction of its network of interactions. The sheer complexity
since they are already examined in recent papers, such as neuro-
of such crucial signalling system is intuitively evident from the
degenerative disorders [44,45] and apoptosis [38]. The rationale is
intricate network of interactions among input and output signals,
hidden in the different levels of analysis, from integrated to single
mediator proteins and feedback loops. Many proteins (in red)
effects, which concur to the determination of the aging phenotype.
directly involved in such interactome are in turn products of genes
3.1. Inflammation that are controlled and regulated in their expression by NF-(cid:1)B.
Framing structure and dynamics of the NF-(cid:1)B interactome in a
Inflammation is a systemic physiological process fundamental
wider, systemic picture will be a significant step toward a better
for survival, which involves a variety of cells, organs and organ
understanding of how it globally regulates diverse gene programs
systems, as depicted in Fig. (3).
and phenotypes. This fact gives rise to the necessity of a systemic,
multiscale approach able to take into consideration all the crucial
levels of complexity into an unique framework, from protein-
protein interactions to gene expression, from cellular to tissue

response and finally to the dynamics of organs.

3.2. Coagulation

Coagulation is a typically complex, multiscale process

involving in a delicate and time-evolving balance a number of

cellular and molecular components. Cells (platelets), protein fac-
tors, cofactors and regulators actively participating through diffe-
rent pathways converge into the coagulation cascade to yield the
conversion of fibrinogen to fibrin, the building block of a hae-

mostatic plug. The haemostatic process is finally down-regulated by
the intersection of the cascade with anticoagulant pathways.
Aging is associated with an increase of many proteins of blood
coagulation and with an impairment of the fibrinolysis process, an
alarming framework that opens the way for thromboembolic and

vascular diseases. Centenarian individuals are characterized by a

state of hyper- or pro-coagulability, and by the possession of
atherothrombotic risk markers and high-risk alleles. Paradoxically,
this condition appears to be compatible with longevity and health.

Experimental evidence shows that the increased activity of

coagulation enzymes in centenarians does not necessarily correlate
Fig. (3). Inflammaging as a systemic phenomenon. Many organs and with augmented thrombosis risk [63-66]. Such data on homeostasis
organ systems of the body are strictly interconnected and together contribute profiles of long-lived and centenarian individuals highlight once
to the increasing low-grade inflammation that occurs with age. again the pressing need for a systemic and multidisciplinary app-
roach to complex and multiscale phenomena such as coagulation
Despite the well-recognized systemic character of inflam- and, at a higher level, aging.
mation, our knowledge of this multi-factorial phenomenon is highly In this perspective, systemic and quantitative modelling
fragmented and its comprehensive understanding is still in its strategies have been successfully applied to uncover coagulation
infancy [55]. cascade kinetics and platelet homeostasis. Luan and co-workers
In the aging field, this consideration is particularly important [67] built a mechanistic model of the human coagulation cascade
for “inflammaging”, [46,56,57], which appears to be important in that, despite the uncertainty inbuilt in modelling such a complex
the pathogenesis of cardiovascular diseases (CVD), type 2 diabetes and multi-level system, has been able to identify critical points in
Systems Biology and Longevity Current Pharmaceutical Design, 2010, Vol. 16, No. 7 807

Fig. (4). A partial reconstruction of the NF-(cid:3)B transcription factors interactome. Proteins involved in the NF-(cid:3)B pathway (data extracted from literature)
are grouped on the basis of their main functional category (each category is inscribed in dashed squares). Direct interactions among proteins are shown (solid
blue lines). Proteins coloured in red derive from genes whose expression is directly regulated by NF-(cid:3)B transcription factors.
the formation of thrombin. Indeed, simulations performed on a generate novel predictions and provide further valuable expla-
literature-derived network of 92 protein complexes and 148 kinetic nations, highlighting for example that, in the ADP-dependent
interactions yielded platelet activation and thrombin generation platelet activation, thrombin is a more potent agonist than ADP,
profiles consistent with experimentally measured parameters. These thanks to differences in receptor copy number. Thus, the power of
results have led to determine which processes can be considered as this integrated computational approach relies on the capability to
“points of fragility”, i.e. if and to which extent they are sensitive or yield, through recursive refinements and developments, more and
not to parameters changes. The model-based screening has thus more accurate predictions of known as well as unknown pheno-
qualitatively identified fragile and robust key mechanisms and mena in platelet biology.
processes, which can be considered as putative drug targets,
especially when concomitantly supported with thrombosis treat- 3.3. Proteasome
ment literature and clinical trials data. This represents a clear The proteasome is an additional candidate target to be studied
example of the therapeutic potentiality of the systemic approach in in a systems biology perspective. It is a self compartimentalized
this field. protease that could be considered an organelle, conserved during
Furthermore, as reported by Purvis and colleagues [68], existing evolution, which is involved in the regulation of all the metabolic
kinetic information for 70 species, 132 kinetic rate constants and 77 pathway, in the production of MHC I epitopes and in the protein
reactions was computationally combined with experimental results quality control, suggesting its centrality in maintaining the cellular
and published data derived from different sources. This integrative homeostasis and thus contributing to reach longevity. Even if it acts
approach was able to predict key parameters in resting and acti- at the molecular level, its effects are relevant at systemic level. The
vated platelets, replicating experimental data at both averaged 26S proteasome consists of a catalytic 20S core (a four-ring
platelet and single platelet response. The model was also able to structure with seven different subunits in each ring, arrayed as
(cid:1)7(cid:2)7(cid:2)7(cid:1)7) and two 19S regulatory complexes. Alternatively,
808 Current Pharmaceutical Design, 2010, Vol. 16, No. 7 Cevenini et al.
proteasome activator PA28-(cid:1)(cid:2) or -(cid:3) can bind to the end of the 20S promotes cellular resistance to oxidants and confers extension of
core [20,69]. In cells exposed to pro-inflammatory stimuli such as human fibroblasts lifespan [83]. A similar stimulatory effect has
IFN-(cid:3) and TNF-(cid:1), the constitutive (cid:2) subunits with catalytic activity been observed by an algae extract on human keratinocytes, as it
((cid:2)1, (cid:2)2, (cid:2)5) are replaced by homologue subunits ((cid:2)1i or LMP2, (cid:2)2i provides protection from UVA and UVB irradiation [84], and by
or MECL-1 and (cid:2)5i or LMP7, respectively) and incorporated into a Tocopherol (Vitamin E) and linoleic acid [85].
newly synthesized alternative proteasome form, the immunoprotea-
some. More recently, a variant, named thymoproteasome, has been 3.4. PON-1
described in thymus [70]. Because constitutive and inducible (cid:2) In this final example, the systems biology approach is applied at
catalytic subunits often coexist in cells, the presence of intermediate a single gene level, which can have a great impact on several age-
type proteasomes with asymmetric subunit composition, different related diseases and longevity as proposed by the conceptualization
proteolytic activities and subcellular localization, have been also of the “diseasome” [86]. Human serum Paraoxonase1 (PON1) is
reported [71]. Considering the broad range of proteasome substrates considered not only for its role in aging: PON1 is one of the most
and the pivotal role of proteasomes in many cellular pathways, studied gene regarding cardiovascular risk, oxidative stress and
alteration in the ubiquitin-proteasome system likely leads to the inflammation. PON1 is primarily synthesised by the liver and high
dysregulation of homeostasis and the development of multiple density lipoprotein (HDL) is the serum transport vector for this
pathologies [72]. An age-related loss of proteasome function has enzyme that is firstly inserted into the external membrane of the
been reported in several human tissues (muscle, lens, lymphocytes secreting cell, and then transferred to HDL [87]. The main function
and epidermis), in aged tissues of other mammals (mice, rats and of paraoxonase is to inhibit low density lipoprotein (LDL) oxidation
bovines), in human primary cultures undergoing senescence [73] as by destroying the biologically active phospholipids in oxidatively
well as in different neurodegenerative pathologies (Alzheimer – modified LDL [88,89]. PON1 significantly reduces the ability of
AD-, Parkinson –PD-, Huntington –HD- disease and Amyotrophic oxidized LDL to trigger monocyte-endothelial cell interactions
Lateral Sclerosis -ALS-) [12,13]. To date, however, several aspects [90]. Also, it hydrolyzes oxidized LDL-associated compounds,
of the proteasome biology and functionality (i.e proteasome which stimulate the production of cytokines, interleukin-8 and
interaction networks, proteasome composition in different tissues monocyte colony-stimulating factors and induce adhesion of
and organs, degradation rate, modulators, among other) are monocytes to the endothelial surface [91,92]. PON1 activity
fragmentary and remain elusive. A very recent interaction map of contributes to prevent the formation of atheromatous plaques. There
the yeast 26S proteasome [74] and novel algorithm for subnetwork is a growing body of evidence that reduced activity of the HDL-
reconstruction in Saccharomyces Cerevisiae [75] have shown the associated enzyme paraoxonase is predictive of vascular disease in
involvement of proteasome in pivotal cellular processes. However, humans, including results from prospective studies [93,94]. Low
a systems biology approach to study the proteasome in both normal levels of PON1 activity have been found in numerous pathological
and pathological conditions, is still in its infancy. A first attempt to conditions, such as cardiovascular disease, type1 and 2 diabetes,
describe, in a systems biology framework, the ubiquitin-proteasome hypercholesterolemia, metabolic syndrome and renal failure [95-
system in normal homeostasis as well as during aging, has been 99]. All these pathologies share a pro-inflammatory baseline
reported by Proctor and co-worker [76], while a more recent model condition and it has now been shown that HDL composition is
of on-going conflicts between the aggregation of (cid:1)-synuclein altered during the acute phase response of the innate immune
((cid:1)SN) and the maintenance of a functional proteasome in PD has system. Polymorphisms located in PON1 coding region are due to
been described [77]. Concerning the knowledge of proteasome an aminoacid substitution at position 192 (Gln-Arg = Q-R alleles)
degradation processes, further insight are provided by an in silico and at position 55 (Leu-Met = L-M alleles). The Q192R poly-
mathematical model, focused on in vitro proteasome degradation morphism imparts different catalytic activity toward some organ
assay, developed by our group [78]. This model, named phosphorus (OP) substrates. Specifically, the 192R isoform
ProteaMAlg (Proteasome Modeling Algorithm), is an extension of a hydrolyzes more efficiently paraoxon, however the 192Q isoform is
previous one [79] and it describes the kinetics of specific protein more efficient in hydrolyzing diazoxon, soman, and sarin
fragments generated by 20S proteasomes in different conditions, [100,101]. Repeatedly, a large number of studies have suggested an
such as the in presence of constitutive or immunoproteasome and association between major cardiovascular diseases and the 192 R
the activator PA28-(cid:1)(cid:2). Thus, in a systems biology framework, the variant. Moreover, the results of a recent meta-analysis [102] show
tool ProteaMAlg could offer a quantitative scheme to study the that PON1 gene variants at codon 192 impact on the probability of
degradation dynamics in different experimental conditions defined attaining longevity, and that subjects carrying RR and QR
by the substrate specificity and the type of proteasome. Other genotypes (R+ carriers) are favoured in reaching extreme ages.
important aspect of proteasome degradation mechanisms, such as These results likely represent the counterpart of the effects observed
the peptide translocation, which can have a great impact on the on CVD, as centenarians and nonagenarians escaped or delayed the
quality of epitope production, has been recently modeled [21,80]. onset of the major age-related diseases, including CVD.
Lastly, another issue to be addressed in a systems biology The role of PON1 emerges out of this brief picture: it can be
perspective is the identification and characterization of molecules located at the centre of a complex interaction of physiological
that could modulate (enhancing or inhibiting) the proteasome processes, impacting on many human complex disease, and thus the
activity leading to the development of proteasome-based drugs able best starting point for a systems biology analysis.
to counteract aging and age-related pathologies. Indeed, at present,
To this regard, we reconstructed two PON1 networks, using
the only available proteasome-based therapeutic approach is the
Cytoscape and Agilent literature search plugin. This plugin takes as
administration of proteasome inhibitors such as Bortezomib, in the
input a set of genes of interest, extracts interactions and generates
treatment of different cancers [81] while new compounds have
putative networks from literature.
recently shown to be able to discriminate between these two main
The first network shown in Fig. (5) is built using only PON-1 as
isoforms of 20S proteasome, constitutive and immunoproteasome
[82]. The identification of natural/synthetic molecules able to input, and displays mainly the proximal nodes that have a direct
enhance proteasome activity (and maybe with higher affinity for connection with it.
different proteasome subtypes) would allow to target other type of As expected, PON-1 is directly connected to APO-E, but it is
diseases where proteasome activity should be enhanced and not worth noting that PON-1 is also directly connected to SP1 and
inhibited. For example, oleuropein, the most abundant of the PPARG (two broadly acting transcription factors which are
phenolic compounds in Olea europaea leaf extract, olive oil, and sensitive to oxidative stress), to PSMC6 (a subunit of the protea-
olives has a stimulatory impact on proteasome activities in vitro, some), to HSD11B1 (a gene encoding a microsomal enzyme that
Systems Biology and Longevity Current Pharmaceutical Design, 2010, Vol. 16, No. 7 809

Fig. (5). Basic PON-1 literature search-based network. This network shows PON-1 first degree interactions, highlighting important connections between
PON-1 and APO-E, SP1, PPARG, PSMC6, HSD11B1 and SREBF-2.

Fig. (6). Extended PON-1 literature search-based network. This expanded network shows PON-1 long-range interactions indicating how PON-1 can affect
a number of different physiological processes.
catalyzes the reversible conversion of the stress hormone cortisol to In this network is possible to see long range influences of PON1 in
the inactive metabolite cortisone) and to SREBF-2 (a transcription different physiological aspects.
factor that controls cholesterol homeostasis). All these genes are
The presence of an inflammatory cluster can be noted,
involved in oxidative stress, cardiovascular diseases and many other
including a master regulator of inflammation, NF-(cid:1)B, and different
age-associated pathologies. inflammatory mediators, such as IL-1 beta and IL-15. In addition,
The second network shown in Fig. (6) is built using the PON1 the presence of a cluster regarding the production of lipophylic
interacting genes of the first network as the input set and it displays hormones and modulators of inflammation can be noted. Indeed,
the connections among the PON1 interacting genes and other genes. this cluster contains enzymes that catalyzes the production of
810 Current Pharmaceutical Design, 2010, Vol. 16, No. 7 Cevenini et al.
prostaglandins (AKR1C3) or steroids (HSD11B1, SRD5A2, a systems biology approach, in a recursive process of “wet” and
CYP11A1, CYP21A2, HSD3B2, HSD17B3, CYP19A1). “dry” experiments, as represented in Fig. (7).
The central cluster is related to the control of cell cycle and Indeed, the existing information and new data generated by ad
proliferation and it is connected to a cluster of epithelial hoc experiments are put together in a comprehensive data set,
differentiation related genes by epidermal growth factor (EGF). which is then analysed and used to generate predictive mathema-
This cluster is also connected to a cluster of vascular remodelling tical models. These new models can suggest new experiments,
related genes (VEGF) by some oxidative and stress response related which in turn produce an enlarged data set and the analytic process
genes such as ABL2, underlining the connection between oxidative restarts. This innovative approach can be performed only if specific
stress response and vascular remodelling. expertise of analysis and computational power will be available,
joint to more collaboration across disciplines and countries. We
4. CONCLUSIONS surmise that this integrated approach will let us understand the
In this complex scenario we briefly summarized, it is emerging aging process at organism level and to identify targets for anti-
that systems biology is a powerful tool to integrate and quantify aging interventions.
high dimensionality but still fragmented data that are produced by
increasing high-throughput technologies in the field of genomics, ACKNOWLEDGEMENTS
proteomics, metabolomics and other –omics. In particular, our This work was supported by: the EU Projects "Proteomage"
laboratory is producing these data in the framework of several (FP6-518230) and "MARK-AGE" (FP7-200880) to CF; Roberto
integrated projects on human aging and longevity financed by and Cornelia Pallotti Legacy for Cancer Research Grants to CF and
European Commission. Some are already concluded, such as SS; University of Bologna “Progetti Strategici” 2006 grant (“p53 e
ECHA, T-CIA, FUNCTIONAGE, PROTAGE, and others are still patologie non neoplastiche nell'anziano: uno studio multidis-
in progress, including GEHA, PROTEOMAGE, MARK-AGE, ciplinare sul ruolo del polimorfismo al codone 72 del gene TP53”)
MYOAGE, RISTOMED and WhyWeAge. We propose that a to SS. AZ acknowledges the UCLH/UCL NIHR Comprehensive
comprehensive analysis of this information is possible only through Biomedical Research Centre and VW foundation.

Fig. (7). The “dry”/"wet” recursive spiral of the systems biology of aging and its implementation.
Systems Biology and Longevity Current Pharmaceutical Design, 2010, Vol. 16, No. 7 811
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